Advanced handwriting identification: Triboelectric sensor array integrating with deep learning toward high information security

Abstract

Handwriting identification is widely accepted as scientific evidence. However, its authenticity is questioned because it depends on the appraiser's professional skills and susceptibility to deliberate false identification by expert witnesses. Consequently, there is an urgent need for an effective handwriting identification system (HWIS) that reduces reliance on the appraiser's skills and mitigates the risk of international false identification. Here, we report a HWIS that integrates a self-powered handwriting signal data acquisition device with an advanced deep learning architecture possessing powerful feature extraction ability and one-class classification function. The device successfully captures the characteristic differences in handwriting behavior between genuine writers and forgers, and the handwriting identification results demonstrate the excellent performance of our system, showcasing its powerful potential to solve the longstanding challenge of handwriting identification that has perplexed humans for a considerable period. Moreover, this work exhibits the system's capability for remote access and downloading the handwriting signal data through the data cloud, highlighting its practical value for fulfilling the requirements of handwriting recognition and identification applications, and it can effectively advance signature information security and ensure the protection of private information.

Keywords

deep learning / handwriting identification / information security / traced handwriting / triboelectric sensor

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